Methods and systems for a parking assist system

ABSTRACT

A parking assist system wherein the system includes a sensor device configured to detect parking space data and transmit it to a post device, wherein the sensor device includes an energy storage device, and a communication device. The communication device may include a camera. The system includes a post device in communication with the sensor device, the system configured to collect parking space data transmitted from a sensor device and communicate parking space occupancy to a driver, wherein the post device includes a vertical post, an energy storage device, a communication device, and a light-emitter. The system may utilize one or more machine-learning algorithms and generate one or more machine-learning models to detect parking space occupancy.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. ProvisionalPatent Application Ser. No. 62/984,953, filed on Mar. 4, 2020, andtitled “METHODS AND SYSTEMS FOR A PARKING ASSIST SYSTEM FIELD OF THEINVENTION,” which is incorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of transportation.In particular, the present invention is directed to methods and systemsfor a parking assist system.

BACKGROUND

Locating an available parking space in a crowded parking lot can bechallenging. Frequently, it can take an average driver 10 or moreminutes to search for an open parking space in a parking lot. This cancause motorists to become frustrated, and result in motorists avoidingshops due to parking challenges. Additionally, parking log congestionattributes to pollution and adds excess carbon dioxide emissions intothe atmosphere. There remains to be seen a system that alerts drivers toopen parking spaces, such as those found in a parking lot, off-street,outdoor, and in an open-air parking lot just to name a few.

SUMMARY OF THE DISCLOSURE

In an aspect a solar parking assist system includes a sensor deviceconfigured to detect parking space data as a function of a camera and anenergy storage device, and transmit the parking space data to a postdevice as a function of a communication device, and a post device,wherein the post device comprises a vertical post, a light emitter, anda solar panel, communicatively connected to the sensor device, whereinthe post device is configured to collect parking space data as afunction of the sensor device, and communicate a parking space occupancyto a driver as a function of the collected parking space data using thecommunication device and the light-emitter.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a diagrammatic representation of an exemplary embodiment of aparking assist system;

FIGS. 2A-2C are diagrammatic representations of an exemplary embodimentof a sensor device;

FIG. 3 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 4 is a block diagram illustrating an exemplary embodiment of aneural network;

FIG. 5 is a block diagram illustrating an exemplary embodiment of a nodein a neural network;

FIG. 6 is a process flow diagram illustrating an exemplary embodiment ofa solar parking assist method; and

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Referring now to FIG. 1 , an exemplary embodiment of a system 100 for aparking assist system is illustrated. Parking assist system includes asensor device 104. A “sensor device,” as used in this disclosure, is anydevice, module, machine, and/or subsystem that is configured to detectevents or changes in the environment and transmit changes in theenvironment to other electronics. A sensor device 104 may detect achange in the environment, that specifies if a parking spot located in aparking lot is occupied by a vehicle. A sensor device may include anysensor including for example, an acoustic sensor, a sound sensor, avibration sensor, an automotive sensor, a chemical sensor, an electriccurrent sensor, an electric potential sensor, a magnetic sensor, a radiosensor, an environment sensor, a flow sensor, a fluid velocity sensor,an ionizing radiation sensor, a subatomic particle sensor, an opticalsensor, a light sensor, an imaging sensor, a photon sensor, a thermalsensor, a heat sensor, a temperature sensor, a proximity sensor, apresence sensor, a speed sensor and the like. A vehicle may include anymeans of carrying or transporting something. A vehicle may include amotor vehicle such as a motorcycle, car, truck, and/or bus. A vehiclemay include a railed vehicle such as a train or tram. A vehicle mayinclude a watercraft such as a ship or boat. A vehicle may include anamphibious vehicle such as a screw propelled vehicle or hovercraft. Avehicle may include an aircraft such as an airplane, helicopter, and/orspacecraft.

Still referring to FIG. 1 , sensor device may include a camera mountedon a post device as described below in more detail. A “camera,” as usedin this disclosure, includes any optical instrument used to record animage. A camera may include a single-lens reflex camera, a large-formatcamera, a medium-format camera, a compact camera, a rangefinder camera,a motion picture camera, a digital camera, a camera phone, a video, andthe like. As a further non-limiting example, cameras may include one ormore compact digital cameras, digital SLR cameras mirrorless cameras,action cameras, 360 cameras, film cameras, and the like thereof. As afurther non-limiting example, camera may include one or more Camera mayinclude a plurality of optical detectors, visible photodetectors, orphotodetectors, where an “optical detector,” “visible photodetector,” or“photodetector” is defined as an electronic device that alters anyparameter of an electronic circuit when contacted by visible or NIRlight. Optical detectors may include, without limitation, charge-coupleddevices (CCD), photodiodes, avalanche photodiodes (APDs), siliconphoto-multipliers (SiPMs), complementary metal-oxide-semiconductor(CMOS), scientific CMOS (sCMOS), micro-channel plates (MCPs),micro-channel plate photomultiplier tubes (MCP-PMTs), single photonavalanche diode (SPAD), Electron Bombarded Active Pixel Sensor (EBAPS),quanta image sensor (QIS), spatial phase imagers (SPI), quantum dotcameras, image intensification tubes, photovoltaic imagers, optical flowsensors and/or imagers, photoresistors and/or photosensitive orphoton-detecting circuit elements, semiconductors and/or transducers.APDs, as used herein, are diodes (e.g. without limitation p-n, p-i-n,and others) reverse biased such that a single photon generated carriercan trigger a short, temporary “avalanche” of photocurrent on the orderof milliamps or more caused by electrons being accelerated through ahigh field region of the diode and impact ionizing covalent bonds in thebulk material, these in turn triggering greater impact ionization ofelectron-hole pairs. APDs may provide a built-in stage of gain throughavalanche multiplication. When a reverse bias is less than breakdownvoltage, a gain of an APD may be approximately linear. For silicon APDsthis gain may be on the order of 10-100. The material of the APD maycontribute to gains.

In an embodiment, and still referring to FIG. 1 , camera may contain itsown dedicated post device located in the middle of a parking lot. Inanother embodiment, camera may share a post device with one or moreadditional cameras that are oriented at one or more viewing angles.Additionally or alternatively, camera may include any existing camerathat may be located in a parking lot, such as those installed forsurveillance and/or security reasons. In an embodiment, a camera mayinclude one or more processors. A processor includes any processor asdescribed herein. In an embodiment, a sensor device may include one ormore processors. A processor includes any processor as described herein.

With continued reference to FIG. 1 , a sensor includes an energy storagedevice 108. A “energy storage device,” as used in this disclosure, is acollection of one or more cells whose chemical reactions create a flowof electrons in a circuit. An energy storage device may include a devicethat stores or delivers energy to be released in the form of electricalpower, including without limitation a battery, fuel cell, capacitor, orother device typically used for storing and/or delivering electricalpower. An energy storage device 108 may include an anode, a cathode, andan electrolyte that reacts with the anode and cathode. An energy storagedevice 108 may include a separator that may prevent an anode and cathodefrom touching. An anode may include a type of electrode from whichelectrons flow out of, when connected to a circuit. A cathode mayinclude a type of electrode from which electrons flow into, whenconnected to a circuit. An electrolyte may include a substance such as aliquid or gel, that is capable of transporting ions between chemicalreactions that occur at the anode and the cathode. An electrolyte mayalso inhibit the flow of electrons between the anode and the cathode, sothat the electrons more easily flow through the external circuit ratherthan through the electrolyte. A separator may include any porousmaterial that may prevent an anode and a cathode from touching, whichwould cause a short circuit in an energy storage device 108. A separatormay be made of a variety of materials, including cotton, nylon,polyester, cardboard, and/or synthetic polymer films. A separator maynot chemically react with either an anode, cathode, or electrolyte. Anenergy storage device 108 may contain a casing, which may include anyhousing or shell that may hold any internal component of an energystorage device 108. A casing may be composed of one or more materialsincluding plastic, steel, soft polymer laminate pouches, and the like.An energy storage device 108 may contain stored energy from one or morefuel base 128 d sources such as coal, oil, gas, and/or nuclear. Anenergy storage device 108 may contain stored energy from one or morerenewable sources such as solar, tidal, and/or wind sources of energy.

With continued reference to FIG. 1 , a sensor may include a magnetometer112. A “magnetometer,” as used in this disclosure, is any device thatmeasures magnetism. Magnetism, may include the direction, strength,and/or relative change of a magnetic field at a particular location. Amagnetometer 112 may include vector magnetometer 112 that measure thevector components of a magnetic field. A magnetometer 112 may include atotal field magnetometer 112 and/or a scalar magnetometer 112 thatmeasure the magnitude of the vector magnetic field. An absolutemagnetometer 112 may measure the absolute magnitude or vector magneticfield, using an internal calibration or known physical constants of amagnetic sensor. A relative magnetometer 112 may measure magnitude orvector magnetic field relative to a fixed but uncalibrated base 128line. A stationary magnetometer 112 may be installed at a fixed positionand may take measurements while a magnetometer 112 is stationary. Aportable magnetometer 112 may be used while in motion and may bemanually carried or transported in a moving object. A magnetometer 112may detect magnetism using one or more operating principles includingbut not limited to a superconducting quantum interference device(SQUID), inductive pickup coils, vibrating sample magnetometer 112(VSM), pulsed-field extraction magnetometry, torque magnetometry,faraday force magnetometry, optical magnetometry, scalar magnetometer112, vector magnetometer 112, and the like. A magnetometer 112 mayinclude any other sensor suitable for detecting occupancy, includingoptical and/or weight sensors.

With continued reference to FIG. 1 , a sensor device 104 may be includedin system 100; sensor device may include without limitation a controlcircuit and/or computing device. Sensor device 104 may include acommunication device 116. A “communication device,” as used in thisdisclosure, is any device that is capable of transmitting and receivingelectronic communication. A communication device 116 includes atransmitter that is capable of generating a wireless signal, which mayinclude without limitation a signal transmitted via electromagneticradiation such as radio waves. As a non-limiting example, a transmittermay generate a radio frequency alternating current that may be appliedto an antenna to radiate radio waves. A transmitter may provide radiocommunication of information over a distance. Information provided to atransmitter may be in the form of an electronic signal. A transmittermay combine information in the form of an electronic signal to becarried with a radio frequency signal which generates radio waves, alsoreferred to as a carrier signal. Information in the form of anelectronic signal may be added to a transmitter through amplitudemodulation, frequency modulation, and other forms of modulation. A radiosignal from a transmitter may be applied to an antenna, which radiatesenergy as radio waves. A transmitter may include one or more componentsthat include but are not limited to a power supply, an electronicoscillator, a modulator, a radio frequency amplifier, and/or animpedance matching circuit. A communication device 116 includes areceiver, that is configured to receive radio waves and convertinformation carried by them to a usable form. A receiver may include anantenna that may intercept radio waves including electromagnetic wavesand convert them to alternating currents that are applied to thereceiver whereby the receiver extracts desired information. A receivermay use electronic filters to separate desired radio frequency signalfrom other signals received by an antenna. A receiver may include anelectronic amplifier that may increase the power of a signal for furtherprocessing and may recover desired information through demodulation. Areceiver may be connected to an antenna which converts energy from anincoming radio wave into radio frequency voltage which may then beapplied to a receiver's input. An antenna may include an arrangement ofmetal conductors and may include the same antenna found in atransmitter. In an embodiment, a transmitter and a receiver may becombined and share a common circuitry and a single housing such as witha transceiver.

With continued reference to FIG. 1 , communication device may includeany wireless form of communication technology, including for example,wi-fi technology, Bluetooth technology, Zigbee, cellular networks,WiMAX, G.hn, and/or ethernet. For example, and without limitation,communication device may include one or more low-power wide-areanetworks, wherein the network may include a network only available tothe users in the parking lot. As a further non-limiting examplecommunication device may include one or more radio signals.

With continued reference to FIG. 1 , parking assist system 100 includesa post device 120 in communication with a sensor device 104. A “postdevice,” as used in this disclosure, includes any device configured tocollect parking space data transmitted from a sensor device 104, andcommunicate parking space occupancy to a driver. A post device 120,includes a vertical post 124. A “vertical post,” as used in thisdisclosure, includes any pole or other elongate structure fixed in anupright position. A vertical post 124 may be used as a point ofattachment for other components of a post device 120, as described inmore detail below. A vertical post 124 may be composed of one or morematerials, including timber, metal, steel, iron, aluminum, magnesium,copper, brass, bronze, zinc, titanium, tungsten, adamantium, nickel,cobalt, tin, lead, silicon, and the like. A vertical post 124 may becomposed of one or more recyclable materials including glass, paper,cardboard, metal, plastic, textiles, and the like. A vertical post 124may be of a certain height to be visible above the height of a vehicle,so that a driver driving throughout a parking lot can see the top of avertical post 124 indicating parking space occupancy above the height ofvehicles parked within a parking lot. In an embodiment, a vertical post124 may be connected to a base 128 at one end, where the base 128 inconfigured to support the post device 120. In an embodiment, the base128 may include a screw in base 128, which may be utilized when postdevice 120 may not be able to be installed into the ground. In anembodiment, base 128 may include a concrete base that may be utilized tosupport the post device 120.

With continued reference to FIG. 1 , post device 120 includes an energystorage device 108. An energy storage device 108 may include any devicesuitable for use as an energy storage device 108 in sensor device 104 asdescribed above. Post device 120 includes a communication device 116.Communication device 116 may include any device suitable for use as acommunication device 116 as described above.

With continued reference to FIG. 1 , post device 120 includes alight-emitter 132, which may include as a non-limiting example alight-emitting diode (LED) display. A “light-emitter,” as used in thisdisclosure, includes any panel display that utilizes light emittingdiodes, or any other electronic components and/or devices as pixels forvideo display. Light-emitter 132 may be utilized within post device 120to provide general illumination and/or visual display. In an embodiment,and as a non-limiting example, Light-emitter 132 may indicate theavailability of parking spaces located within a parking lot. Forinstance and without limitation, Light-emitter 132 may illuminate andappear to be a green color when there are open parking spaces availablewithin a certain location of a parking lot, whereas Light-emitter 132may not illuminate and may be, as a non-limiting example, a black colorwhen there are no parking spaces available within a certain location ofparking lot. In an embodiment, and without limitation, light-emitter 132may generate a visual display that assists drivers in identifying aparking spot. For example, and without limitation a first light emittermay display a first green color, wherein a second light emitter may thendisplay a second green color indicating the driver is getting closer tothe parking spot.

With continued reference to FIG. 1 , post device 120 may include a solarpanel 136. A “solar panel,” as used in this disclosure, is any paneland/or other component or device that absorbs sunlight or other visibleand/or invisible electromagnetic radiation as a source of energy togenerate direct current electricity. Solar panel 136 may include aphotovoltaic module that utilizes light energy or photons from the sun,to generate electricity through a photovoltaic effect. A photovoltaicmodule may utilize wafer-based 128 crystalline silicon cells and/or thinfilm cells. A solar panel 136 may be produced from crystalline siliconsolar cells. Solar panel 136 may be produced from one or more recycledmaterials. In an embodiment solar panel 136 may include an absorption oflight that generates either electron-hole pairs and/or excitons. Forexample, and without limitation, solar cell 136 absorb photons from alight source, wherein electron-hole pairs are generated as a function ofdoped silicon. Solar panel 136 may excite electrons from a first atomicorbital to an excited atomic orbital, wherein the electron may dissipatethe energy as heat and return to its first atomic orbital and/or travelthrough the solar cell until it reaches the electrode to generate acurrent. Solar panel 136 may convert solar energy into a usable amountof direct current electricity and/or alternating current as a functionof an inverter.

With continued reference to FIG. 1 , post device may receive datatransmitted from communication device such as a camera. In anembodiment, communication device such as a camera may store data thatmay be subsequently transmitted to post device utilizing an “on” or“off” input. For example, an “on” input may signify to communicationdevice, to transmit data to post device, while an “off” input maysignify to communication device, not to transmit data to post device.

With continued reference to FIG. 1 , one or more sensor device 104 maybe placed within one or more parking spaces located within a parkinglot. Sensor device 104 may detect an open parking space and transmitdata regarding the opening parking space to post device 120 utilizing acommunication device 116. For example, a transmitter may generate anelectronic signal indicating a sensor located in a first parking spacehas detected that the first parking space is available and is notoccupied by a vehicle. Electronic signal may be received by a receiverlocated within post device 120. Post device 120 may then illuminate aLight-emitter 132 to inform a driver about an open parking space. In anembodiment, Light-emitter 132 may illuminate a green color light toinform a driver that a parking space is available. In an embodiment,Light-emitter 132 may illuminate in relation to the location of an openparking space in a parking lot. For example, post device 120 may belocated in the middle of two rows within a parking lot. In such aninstance, post device 120 may be in communication with sensor device 104located in both rows, whereby Light-emitter 132 may illuminate only asection or portion of the entire Light-emitter 132 to indicate an openparking spot within one of the two rows. For example, a first half of aLight-emitter 132 facing a first parking row may illuminate in a greencolor to indicate an open parking space in the first row facing the halfof the Light-emitter 132, whereas a second parking row that does nothave any parking availability may have a second half of a Light-emitter132 facing a second parking row not illuminated, to indicate there isnot an open parking space in the second row facing the second half ofthe Light-emitter 132.

With continued reference to FIG. 1 , sensor device 104 is configured todetect a parking space occupied by a vehicle and transmit data regardingthe occupied parking space to post device 120 utilizing communicationdevice 116. Post device 120 may then extinguish a light-emitter 132 toinform a driver about an occupied parking space. In an embodiment, alight-emitter 132 that is extinguished may appear to be of a blackcolor. In an embodiment, post device 120 may extinguish a light-emitter132 in location to an occupied parking space within a parking lot. Forexample, in an embodiment, Light-emitter 132 may be of a round, circularshape and may be located at the top of post device 120, situated in themiddle of two rows in a parking lot. In such an instance, a first halfof circular light-emitter 132 may be located in first parking lot row,while a second half of circular light-emitter 132 may be located in asecond parking lot row. In such an instance, a first half of circularlight-emitter 132 may be extinguished to indicate the first parking lotrow contains all occupied parking spaces, while the second half ofcircular light-emitter 132 may be illuminated to indicate one or moreavailable parking spaces within the second parking lot row.

With continued reference to FIG. 1 , vertical post 124 may be connectedto one or more support beams. A support beam may include any structuralelement that may resist loads applied to it. A support beam may becomposed of any material suitable for use as vertical post 124. In anembodiment, a support beam may connect vertical post 124 to alight-emitter 132 and a solar panel 136 that may be located at the topend of vertical post 124. In an embodiment, light-emitter 132 mayinclude a light orb that may be located atop vertical post 124 and maybe in contact with solar panel 136 as described in more detail below.Light orb may be of a round, circular shape and may illuminate orextinguish to indicate parking space occupancy. In an embodiment, asolar panel 136 may surround a light-emitter 132 atop a vertical post124 as described below in more detail.

With continued reference to FIG. 1 , post device 120 may be configuredto receive parking space data from a plurality of sensor device 104wherein each of the plurality of sensor device 104 may be located in adifferent parking space. Post device 120 may then illuminate alight-emitter 132 to indicate the location of an open parking space. Forexample, a post device 120 may illuminate a portion of a light-emitter132 to indicate a particular region of a parking space such as a certainrow of parking spaces or cluster of closely located parking spaces thatmay have availability or that may be occupied. In an embodiment, aninfinite number of sensors located within transmission range may belinked together with one particular post device 120. For example, theremay be as few as one parking space on one side of post device 120 andone parking spaces on other side of post device 120 for a total of twosensors in communication with one post device 120. In yet anothernon-limiting example, there may be as many as twenty parking spaces oneside of post device 120 with one sensor device 104 located within alltwenty parking spaces, and twenty parking spaces on other side of postdevice 120 with an additional twenty sensor device 104 located on otherside of post device 120, for a total of forty parking spaces tied to onepost device 120. In an embodiment, if any parkin space is available oneither side of post device 120, then Light-emitter 132 will illuminateon the corresponding side and indicate parking spot availability withinthe parking spaces tied to post device 120.

Still referring to FIG. 1 , light-emitter 132 may appear as a light orb,having a round structure with LED lighting surrounding the entire lightorb. In an embodiment, light orb may be supported by one or more supportbeams, as described above in detail. Support beams may connect tovertical post 124 and support light orb and solar panel 136 located atopvertical post 124. In an embodiment, post device 120 containing lightorb may be placed in between and in the middle of two parking lot rows.In such an instance, light orb may illuminate and/or extinguish toindicate which of the two parking lot rows has parking availabilityand/or is full to inform a driver of a vehicle. In an embodiment, firsthalf of light orb may be illuminated to indicate parking availability ina first parking row of a parking lot. In such an instance, second halfof light orb may be extinguished to indicate the lack of parkingavailability in a second parking row of a parking lot. In an embodiment,first half of light orb may be outward facing towards first parking rowand second half of light orb may be outward facing towards secondparking row. Additionally or alternatively, light orb may be placed atopvertical post 124. Light orb may illuminate in a green color to indicatethere is parking space availability, and light orb may extinguish andappear as a white color when no parking space is available. Light orbmay also be placed in the middle of vertical post 124, with solar panel136 sitting atop vertical post 124. In an embodiment, light orb may besupported by one or more support beams.

With continued reference to FIG. 1 , post device 120 is configured tocollect data relating to parking space availability and occupancy. Postdevice 120 is configured to assemble parking space data related to aparking lot over a specified time frame. For example, post device 120may assemble parking space data during peak shopping hours for a parkinglot located in a shopping center. Post device 120 may be configured todisplay parking space data related to a parking lot so that a parkinglot owner or store owner may review data relating to the parking lot. Inan embodiment, a parking lot owner or store owner may specify aparticular time period that they seek to obtain parking space data for.Post device 120 is configured to transmit parking space data to a userclient device utilizing a communication device 116. A user client devicemay include without limitation, a display in communication with postdevice 120, where a display may include any display as described herein.User client device may include an additional computing device, such as amobile device, laptop, desktop, computer and the like. User clientdevice may be operated by a driver, operator, and/or passenger of avehicle so that the driver, operator, and/or passenger of the vehiclecan determine parking spot availability before departing or upon arrivalat a parking lot. Post device 120 may be configured to transmit parkingspace data to an autonomous vehicle utilizing a communication device116. An autonomous vehicle may include any vehicle capable of sensingits environment and moving with little or no human input. In anembodiment and without limitation, autonomous vehicle may receive asignal and/or direction as a function of the communication device andtraverse through a parking lot towards an open parking space identifiedby system 100. In another embodiment, autonomous vehicle may receive anotification and/or communication that the parking lot is full. Forexample, and without limitation, communication device 116 may signal toa vehicle and/or autonomous vehicle that a parking lot is full, whereincommunication device 116 recommends one or more subsequent parking lotsthat may or may not have an open parking space for the vehicle and/orautonomous vehicle.

With continued reference to FIG. 1 , parking space data may be assembledat sensor device, such as at a camera location using main power. Sensordevice, including a camera may be configured to transmit parking spacedata to a user client device utilizing communication device 116 forexample. In an embodiment, sensor device may be configured to transmitparking space data to a web-based portal to display the information suchas parking space availability for multiple users to view. In anembodiment, sensor device such as a camera, may be configured to performone or more machine-learning algorithms to detect the presence and/orabsence of vehicles in parking spaces. Machine-learning algorithms maygenerate correlations, mathematical relationships, and/or otherrelationships between inputs of data to outputs of data, which may begenerated from a training set containing a plurality, which may bethousands or millions, of inputs and related output data points; eachinput may be mapped to a related output in training set. Training setdata utilized to generate mathematical algorithms may be obtained frommultiple sources, such as data obtained from studies, journals, and/orother platforms. Sources may include information that is already in thepublic domain, such as data that is open source and available for thepublic to use. Initial training set data obtained for system 100 togenerate initial models may be gathered from any scientific journals,data sets already available in the public domain such as open-sourcedata, and/or from initial information provided by users.

With continued reference to FIG. 1 , machine-learning processes mayfurther be performed as a function of context data, circumstances, data,or other information available to system 100 concerning current or pastinteractions with system 100 and/or third-party processes, platforms, ordevices. Machine-learning algorithms as used herein are processesexecuted by computing devices to improve accuracy and efficiency ofother processes performed by the computing devices through statisticalor mathematical measures of accuracy and efficiency. Machine learningmay function by measuring a difference between predicted answers oroutputs and goal answers or outputs representing ideal or “real-world”outcomes the other processes are intended to approximate. Predictedanswers or outputs may be produced by an initial or intermediate versionof the process to be generated, which process may be modified as aresult of the difference between predicted answers or outputs and goalanswers or outputs. Initial processes to be improved may be created by aprogrammer or user or may be generated according to a givenmachine-learning algorithm using data initially available. Inputs andgoal outputs may be provided in two data sets from which the machinelearning algorithm may derive the above-described calculations; forinstance, a first set of inputs and corresponding goal outputs may beprovided and used to create a mathematical relationship between inputsand outputs that forms a basis of an initial or intermediate process,and which may be tested against further provided inputs and goaloutputs. Data sets representing inputs and corresponding goal outputsmay be continuously updated with additional data; machine-learningprocess may continue to learn from additional data produced when machinelearning process analyzes outputs of “live” processes produced bymachine-learning processes.

With continued reference to FIG. 1 , machine-learning algorithm mayinclude, without limitation, linear discriminant analysis.Machine-learning algorithm may include quadratic discriminate analysis.Machine-learning algorithms may include kernel ridge regression.Machine-learning algorithms may include support vector machines,including without limitation support vector classification-basedregression processes. Machine-learning algorithms may include stochasticgradient descent algorithms, including classification and regressionalgorithms based on stochastic gradient descent. Machine-learningalgorithms may include nearest neighbors algorithms. Machine-learningalgorithms may include Gaussian processes such as Gaussian ProcessRegression. Machine-learning algorithms may include cross-decompositionalgorithms, including partial least squares and/or canonical correlationanalysis. Machine-learning algorithms may include naïve Bayes methods.Machine-learning algorithms may include algorithms based on decisiontrees, such as decision tree classification or regression algorithms.Machine-learning algorithms may include ensemble methods such as baggingmeta-estimator, forest of randomized tress, AdaBoost, gradient treeboosting, and/or voting classifier methods. Machine-learning algorithmsmay include neural net algorithms, including convolutional neural netprocesses. Machine-learning algorithms may include supervisedmachine-learning algorithms. Machine-learning algorithms may includeunsupervised machine-learning algorithms. Machine-learning algorithmsmay include lazy-learning machine-learning algorithms. In an embodiment,any component of system 100 may be configured to generate one or moremachine-learning algorithms.

With continued reference to FIG. 1 , machine-learning processes mayinclude generating one or more machine-learning models. A“machine-learning model,” as used in this disclosure, is a process thatautomatedly uses a body of data known as “training data” and/or a“training set” to generate an algorithm that will be performed such asby a processor located within a camera, to produce outputs given dataprovided as inputs; this is in contrast to a non-machine-learningsoftware program where the commands to be executed are determined inadvance by a user and written in a programming language. For example,and without limitation training data may be used to trainmachine-learning model. In an embodiment, one or more machine-learningalgorithms and/or machine-learning models may be utilized for licenseplate recognition of a vehicle.

In an embodiment, and still referring to FIG. 1 , system 100 mayidentify a vehicle as a function of an identification machine-learningmodel. As used in this disclosure “identification machine-learningmodel” is a machine-learning model to identify a vehicle output givenidentification elements as inputs; this is in contrast to a non-machinelearning software program where the commands to be executed aredetermined in advance by a user and written in a programming language.As used in this disclosure “identification inputs” are inputs associatedwith one or more characteristic qualities of a vehicle. For example, andwithout limitation, license plate numbers, registration numbers, vehiclemodels, vehicle makes, vehicle years, vehicle colors, and the likethereof. Identification machine-learning model may include one or moreidentification machine-learning processes such as supervised,unsupervised, or reinforcement machine-learning processes that system100 may or may not use in the determination of identification a vehicle.An identification machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

Still referring to FIG. 1 , computing device 104 may trainidentification machine-learning process as a function of anidentification training set. As used in this disclosure “identificationtraining set” is a training set that correlates an identificationelement to a vehicle. For example, and without limitation, anidentification element of a black Subaru with a license plate number1NXS16 may relate to a vehicle of an undercover law enforcement officer.The identification training set may be received as a function ofuser-entered identification elements and/or vehicles. System 100 mayobtain identification training set by receiving correlations ofidentification elements that were previously received and/or determinedduring a previous iteration of identifying a vehicle. The identificationtraining set may be obtained in the form of one or more user-enteredcorrelations of an identification elements to a vehicle. For example,and without limitation, a camera may be positioned at one or more anglesto obtain an identification element of the letters located on a licenseplate and identify the vehicle as a function of identificationmachine-learning model using identification training data. For example,and without limitation, a vehicle may be identified as a function ofidentifying one or more license plates associated with individualsand/or government officials. As a further non-limiting example, avehicle may be identified as a function of identifying one or morelicense plates associated with emergency personnel.

In an embodiment, and still referring to FIG. 1 , system 100 mayidentify one or more vehicles and/or license plates by any suitablemethod, including without limitation an image classifier. An “imageclassifier,” as used in this disclosure is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. Image classifier may be configured to output atleast a datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. For example, and without limitation imageclassifier may output one or more identities of vehicles, such as ownersof the vehicle, drivers of the vehicle, insurance companies associatedwith the vehicle, and the like thereof. Computing device 104 and/oranother device may generate a classifier using a classificationalgorithm, defined as a processes whereby computing device 104 derives aclassifier from training data. Classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers.

Still referring to FIG. 1 , computing device 104 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)÷P(B), where P(A/B) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 104 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 104 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1 , computing device 104 may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 1 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute/as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

In an embodiment and still referring to FIG. 1 , computing device mayidentify a vehicle and/or object and automatically pay for the parkingspace. For example, and without limitation image classifier may classifya vehicle to an emergency services vehicle, wherein the parking spacehas a price of $5.00 and is automatically charged to the account ownerfor that vehicle. As a further non-limiting example, image classifiermay classify a vehicle to a consumer vehicle, wherein the parking spacehas a price of $25.00 and is automatically charged to the consumer thatparked in the parking space. In an embodiment, and without limitation,image classifier may classify one or more groups of vehicles that parkwithin close proximity to one another to a single account. For example,a corporation may have 5 company cars that are parked in a single laneof a parking lot, wherein image classifier may classify the vehicles toa single account owner and charge the account owner for the 5 vehiclesthat are classified together.

Referring now to FIGS. 2A-2B, an exemplary embodiment 200 of sensordevice 104 is illustrated. In FIG. 2A, sensor device 104, may beconfigured to detect the presence or absence of a vehicle 204, inparking space 208. A vehicle 204, includes any of the vehicles asdescribed above in more detail in reference to FIG. 1 . Referring now toFIG. 2B, sensor device 104 may include a camera 212 attached to a lightpost 216. Camera may include any camera as described herein. Light post216 may include any pole located in a parking lot, pathway, and/ordriveway. Light post 216 may include any standard parking lot lightpost. Referring now to FIG. 2C, camera 212 may be attached to light post216, where camera 212 is able to detect a vehicle already located inparking space 208.

In an embodiment, and still referring to FIG. 2 , post device 120 may besituated in between rows of parking spaces 208. In an embodiment, postdevice 120 may illuminate light-emitter 132 to appear as a green color,to indicate available parking spaces that are open, within a certainlocation in a parking lot. In an embodiment, light-emitter 132 mayilluminate and/or extinguish light-emitter 132 to indicate a particularrow that does or does not have availability. For example, post device120 may be situated in between two parking rows, and light-emitter 132may be illuminated on a first side to indicate availability in a firstparking row, and light-emitter 132 may be extinguished on a second sideto indicate there is no parking availability in a second parking row.Post device 120 may be of a certain height above vehicles parked in aparking lot, so that a driver of a vehicle may be able to see from adistance, whether a particular row has parking availability or not.

Referring now to FIG. 3 , an exemplary embodiment of a machine-learningmodule 600 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 304 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 308 given data provided as inputs 312;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 3 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 304 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 304 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 304 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 304 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 304 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 304 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data304 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 3 ,training data 304 may include one or more elements that are notcategorized; that is, training data 304 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 304 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 304 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 304 used by machine-learning module 300 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample, identification inputs may result in identifying a vehicle.

Further referring to FIG. 3 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 316. Training data classifier 316 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 300 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 304. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 316 may classify elements of training data tosub-categories of identification elements such as license plates,registrations, colors, makes, models and the like thereof.

Still referring to FIG. 3 , machine-learning module 300 may beconfigured to perform a lazy-learning process 320 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 304. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 304 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 3 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 324. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 324 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 324 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 304set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 3 , machine-learning algorithms may include atleast a supervised machine-learning process 328. At least a supervisedmachine-learning process 328, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude identification elements as described above as inputs, identifiedvehicles as outputs, and a scoring function representing a desired formof relationship to be detected between inputs and outputs; scoringfunction may, for instance, seek to maximize the probability that agiven input and/or combination of elements inputs is associated with agiven output to minimize the probability that a given input is notassociated with a given output. Scoring function may be expressed as arisk function representing an “expected loss” of an algorithm relatinginputs to outputs, where loss is computed as an error functionrepresenting a degree to which a prediction generated by the relation isincorrect when compared to a given input-output pair provided intraining data 304. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 328 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 3 , machine learning processes may include atleast an unsupervised machine-learning processes 332. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 3 , machine-learning module 300 may be designedand configured to create a machine-learning model 324 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 3 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 4 , an exemplary embodiment of a neural network400 is illustrated. Neural network 404 also known as an artificialneural network, is a network of “nodes,” or data structures having oneor more inputs, one or more outputs, and a function determining outputsbased on inputs. Such nodes may be organized in a network, such aswithout limitation a convolutional neural network, including an inputlayer of nodes 404, one or more intermediate layers 408, and an outputlayer of nodes 412. Connections between nodes may be created via theprocess of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Referring now to FIG. 5 , an exemplary embodiment of a node of a neuralnetwork is illustrated. A node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above.

Still referring to FIG. 5 , a neural network may receive images from oneor more sensors as inputs and output vectors representing such imagesaccording to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Referring now to FIG. 6 , an exemplary embodiment of a method 600 of asolar parking assist is illustrated. At step 605, a sensor device 104detects parking space data. A sensor device 104 includes any of thesensor device 104 as described above in more detail in reference toFIGS. 1-5 . A sensor device 104 includes an energy storage device 108,and a communication device 116. A sensor device 104 may include acamera, as described above in more detail in reference to FIGS. 1-3 . Inan embodiment, sensor device 104 such as a camera, may be configured tocollect data and may perform one or more machine-learning algorithms ondata, as described above in more detail in reference to FIGS. 1-5 . Inan embodiment, camera may perform one or more machine-learningalgorithms and may transmit the information to another component ofsystem 100 such as post device, a processor, and/or a cloud where datamay be stored. Cloud includes any of the clouds as described above inmore detail. Parking space data may indicate if a parking space isoccupied by a vehicle or if a parking space is free and is not occupiedby a vehicle. A vehicle includes any of the vehicles as described abovein more detail in reference to FIGS. 1-5 . In an embodiment, sensordevice 104 may detect parking space data utilizing magnetometer 112 todetect magnetic fields located within a parking space. In an embodiment,each parking space located in a parking lot may contain its own sensordevice 104 to detect the absence or presence of a vehicle in eachindividual parking space, at any given time.

With continued reference to FIG. 6 , at step 610, a sensor device 104transmits parking space data to a post device 120 in communication withsensor device 104. Post device 120 includes any of the post device 120as described above in more detail in reference to FIGS. 1-5 . Postdevice 120 includes a vertical post 124, an energy storage device 108, acommunication device 116, a light-emitter 132, and a solar panel 136 asdescribed above in more detail in reference to FIGS. 1-5 . In anembodiment, a plurality of post device 120 may be located a differentlocation within a parking lot. Each post device 120 may be incommunication with one or more sensor device 104 base 128 on thelocation of a post device 120. A sensor device 104 may be incommunication with a post device 120 when a sensor device 104 cantransmit data with a post device 120. In an embodiment, a plurality ofpost device 120 may be placed in a median position in between twoseparate parking rows. In such an instance, each post device 120 maycommunicate with sensor device 104 located in two parking rows. Sensordevice 104 may transmit parking space data to a post device 120utilizing any network methodology as described herein. In an embodiment,sensor device 104 may transmit parking space data from transmitterlocated within communication device 116 located within sensor device104, to a receiver located within communication device 116 locatedwithin post device 120. Transmitting may include transmitting dataincluding any of the data described herein to another processor, acloud, and/or another other component of system 100 before being sent topost device 120. A “cloud,” as used in this disclosure, includes anyon-demand availability of computer system resources, such as datastorage and computing power, without direct active management by a user.A cloud may include one or more data centers available to many usersover the Internet. A cloud may include a private cloud, a public cloud,and/or a hybrid cloud.

With continued reference to FIG. 6 , at step 615, a post device 120 incommunication with a sensor device 104 collects parking space datatransmitted from a sensor device 104. In an embodiment, a post device120 may collect parking space data from one or more sensor device 104.In an embodiment, a post device 120 may assemble parking space datarelated to a parking lot over a specified time frame and display theparking space data related to the parking lot, so that a business owneror owner of the parking lot can determine parking lot use andfunctionality. In an embodiment, post device 120 may transmit parkingspace data to a user client device, which may include any device asdescribed above in more detail in reference to FIGS. 1-5 . Collection ofparking space data may be performed by any component of system 100,including a processor, a cloud independent of post device 120, and/orany sensor including a camera. Any component of system 100 may beconfigured to perform any machine-learning algorithm and/ormachine-learning process as described herein.

With continued reference to FIG. 6 , at step 620, a post device 120 incommunication with a sensor device 104 communicates parking spaceoccupancy to driver. A post device 120 may communicate parking spaceoccupancy utilizing light-emitter 132, as described above in moredetail. For example, post device 120 may receive parking space data froma plurality of sensor device 104 wherein each of the plurality of sensordevice 104 is located in a different parking space in a parking lot.Post device 120 may then illuminate a light-emitter 132 located atoppost device 120 to indicate the location of an open parking space. In anembodiment, light-emitter 132 may appear to have a particular color toindicate to a driver or passenger of a vehicle that there is parkingspace availability within a certain parking lot row. For example,light-emitter 132 may emit a blue color, to indicate that a handicappedparking space may be available, while a non-handicapped parking maycause light-emitter 132 to appear as another color, that may be of aspecial significance to a lot owner. For example, a particular sectionof a parking garage may be cause light-emitter 132 to emit a particularcolor to indicate a section of a parking area such as a parking lotand/or the top floor of a parking garage that may indicate premiumspaces within a parking garage where a subscriber may pay money to parkin the premium spaces because parking may be guaranteed or premiumparking spaces may be located at preferred locations such as near astairwell, elevator, and/or entrance to a building such as a shoppingmall just to name a few. In an embodiment, a portion of a light-emitter132 may be illuminated to indicate a particular location where there isparking space availability. A post device 120 may receive parking spacedata from a plurality of sensor device 104 wherein each of the pluralityof sensor device 104 is located in a different parking space. A postdevice 120 may extinguish a light-emitter 132 located within the postdevice 120 to indicate the absence of an open parking space. Forexample, a light-emitter 132 may dark and appear to black out when thereis no more parking availability within a particular location of aparking lot. In an embodiment, a portion of a light-emitter 132 locatedatop post device 120 may be extinguished to indicate that a particularrow or a particular location of a parking lot has no more parkingavailability. Post device 120 may transmit parking space occupancy to anautonomous vehicle as described above in more detail. Parking spaceoccupancy may be transmitted to an autonomous vehicle utilizing anyprocessor, and/or communication device as described herein. For example,parking space occupancy may be transmitted to an autonomous vehicle by aprocessor located within a communication device such as a camera.Light-emitter 132 may be visible by other passengers in a vehicle tohelp a driver identify where there is parking space availability. In anembodiment, post device 120 may transmit a message to a user clientdevice so that a driver and/or passenger know upon arrival at a parkinglot or upon leaving to drive to parking lot where there are availableparking spaces. Communications regarding parking space occupancy may becommunicated to a driver of a vehicle directly from a processor, and/orfrom information stored in a cloud via communication device.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 12 via a peripheral interface 5576. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A solar parking assist system, the systemcomprising: a sensor device configured to: detect parking space data asa function of a camera and an energy storage device; and transmit theparking space data to a post device, as a function of a communicationdevice, and a post device communicatively connected to the sensordevice, the post device comprising a vertical post, a light emitter, anda solar panel, and wherein the post device is configured to: collectparking space data as a function of the sensor device; receive parkingspace data from a plurality of sensor devices wherein each of theplurality of sensor devices is located in a different parking space;communicate a parking space occupancy to a driver as a function of thecollected parking space data using the communication device and thelight-emitter; and illuminate the light-emitter to indicate the locationof an open parking space.
 2. The system of claim 1, wherein the sensordevice further comprises a magnetometer.
 3. The system of claim 1,wherein the sensor device further comprises a processor.
 4. The systemof claim 1, wherein the sensor device is further configured to: detectan open parking space; transmit data regarding the open parking space tothe post device utilizing the communication device; and illuminate thelight-emitter to inform a driver about the open parking space.
 5. Thesystem of claim 4, wherein the light-emitter is illuminated in relationto the location of the open parking space in a parking lot.
 6. Thesystem of claim 1, wherein the sensor device is further configured to:detect an occupied parking space; transmit data regarding the occupiedparking space to the post device utilizing the communication device; andextinguish the light-emitter to inform a driver about the occupiedparking space.
 7. The system of claim 6, wherein the light-emitter isextinguished in relation to the location of the occupied parking spacein a parking lot.
 8. The system of claim 1, wherein the communicationdevice further comprises: a transmitter configured to transmit a radiocommunication; and a receiver configured to convert informationcontained within the radio communication into a useable form.
 9. Thesystem of claim 1, wherein the communication device further comprises awireless form of communication.
 10. The system of claim 1, wherein thevertical post is connected to a base configured to support the postdevice.
 11. The system of claim 10, wherein the vertical post isconnected to a support beam wherein the support beam connects thevertical post to the light-emitter and the solar panel.
 12. The systemof claim 10, wherein the light-emitter further comprises a light orblocated atop the vertical post and in contact with the solar panel. 13.The system of claim 10, wherein the solar panel is configured tosurround the light-emitter atop the vertical post.
 14. The system ofclaim 1, wherein the post device is further configured to: assembleparking space data related to a parking lot over a specified time frame;and display the parking space data related to a parking lot.
 15. Thesystem of claim 1, wherein the parking assist system is furtherconfigured to transmit parking space data to a user client deviceutilizing the communication device.
 16. The system of claim 1, whereinthe parking assist system is further configured to transmit parkingspace data to an autonomous vehicle utilizing the communication device.17. The system of claim 1, wherein the parking assist system is furtherconfigured to transmit parking space data to a database.
 18. The systemof claim 1, wherein the sensor device is further configured to identifya vehicle.
 19. The system of claim 18, wherein identifying the vehiclefurther comprises: obtaining an identification training set; andidentifying the vehicle as a function of the parking space data using anidentification machine-learning model, wherein the identificationmachine-learning model is trained as a function of the identificationtraining set.